AI-Augmented Offsite Construction: Exploring Utility Theory and Boundary Objects for Design and Cost Optimisation

Off-site construction (OSC) projects face persistent challenges in balancing design performance, cost efficiency, and stakeholder priorities due to fragmented workflows between generative design (GD) and building information modelling (BIM). Although AI-enabled design tools are increasingly adopted, limited integration and inconsistent decision-making continue to restrict effective optimisation and interdisciplinary collaboration. This study investigates how utility theory and boundary object theory can support AI-augmented OSC workflows for design and cost optimisation. Utility theory provides a framework for evaluating trade-offs between competing objectives, while GD and BIM are conceptualised as boundary objects that facilitate communication and coordination among stakeholders. An AI-enabled optimisation approach is applied through two OSC application cases to analyse interactions between design and cost parameters. The findings inform a theoretical model incorporating a boundary-negotiating artefact to support integrated GD–BIM workflows. The model enables iterative design refinement, adaptive optimisation, and alignment of stakeholder priorities. The study illustrates how integrated AI-augmented workflows can improve collaborative decision-making, interoperability, and value optimisation in OSC.